Image-based defects identification and semi-supervised localization

A system for manufacturing defect classification, comprising:

• a first neural network branch receiving a first data as input and generating a first output;
• a second neural network branch receiving a second data as input and generating a second output, wherein first neural network branch and the second neural network branch are trained independently from each other;
• and
• a fusion neural network receiving the first output and the second output and generating the manufacturing defect classification,
• wherein the first data are Energy-Dispersive X-ray Spectroscopy (EDS) images, the first neural network branch further comprising a channel attention module to put additional weight to a channel of the first data, the channel corresponding a target element.

A computer-implemented method for manufacturing defect classification, comprising:

• receiving a first output from a first neural network branch that takes first data as input;
• receiving a second output from a second neural network branch that takes second data as input, wherein the first neural network and the second neural network are trained independently of each other;
• and
• inputting the first output and the second output into a fusion neural network to generate the manufacturing defect classification,
• wherein the fusion neural network comprises a convolutional block attention module having a spatial attention module and a channel attention module.